In [1]:
import scanpy as sc

import numpy as np
import pandas as pd

import os
In [2]:
# Working directory 
os.chdir('/research/peer/fdeckert/FD20200109SPLENO')
In [3]:
# rpy2 
os.environ['R_HOME'] = '/home/fdeckert/bin/miniconda3/envs/p.3.8.12-FD20200109SPLENO/lib/R'
In [4]:
# Plotting 
import rpy2.robjects as robjects
color_load = robjects.r.source('plotting_global.R')
color = dict()
for i in range(len(color_load[0])):
    color[color_load[0].names[i]] = {key : color_load[0][i].rx2(key)[0] for key in color_load[0][i].names}

sc.set_figure_params(figsize=(5, 5))

Seurat SCTransform + 2000 HVG integrated scale.data¶

In [5]:
adata = sc.read_h5ad('data/object/so_sct.h5ad')
adata = adata.raw.to_adata()
In [6]:
sc.tl.pca(adata, n_comps=200)
In [7]:
def set_color(categories): 
    
    categories = [x for x in categories if x in list(adata.obs.columns)]

    for category in categories: 
        
        adata.obs[category] = pd.Series(adata.obs[category], dtype='category')
        
        keys = list(color[category].keys())
        keys = [x for x in keys if x in list(adata.obs[category])]

        adata.obs[category] = adata.obs[category].cat.reorder_categories(keys)
        adata.uns[category+'_colors'] = np.array([color[category].get(key) for key in keys], dtype=object)
        
# Set colors
set_color(list(color.keys()))
In [8]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=200, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [9]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=150, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [10]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=100, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [11]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=90, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [12]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=80, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [13]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=70, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [14]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=60, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [15]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=50, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)

Seurat SCTransform + regression + 2000 HVG integrated scale.data¶

In [16]:
adata = sc.read_h5ad('data/object/so_sct_reg.h5ad')
adata = adata.raw.to_adata()
In [17]:
sc.tl.pca(adata, n_comps=50)
In [18]:
def set_color(categories): 
    
    categories = [x for x in categories if x in list(adata.obs.columns)]

    for category in categories: 
        
        adata.obs[category] = pd.Series(adata.obs[category], dtype='category')
        
        keys = list(color[category].keys())
        keys = [x for x in keys if x in list(adata.obs[category])]

        adata.obs[category] = adata.obs[category].cat.reorder_categories(keys)
        adata.uns[category+'_colors'] = np.array([color[category].get(key) for key in keys], dtype=object)
        
# Set colors
set_color(list(color.keys()))
In [19]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=200, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [20]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=150, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [21]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=100, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [22]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=90, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [23]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=80, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [24]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=70, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [25]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=60, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)
In [26]:
# Dimensional reduction and clustering 
sc.pp.neighbors(adata, n_neighbors=100, n_pcs=50, use_rep='X_pca')
sc.tl.leiden(adata, resolution=1)
sc.tl.louvain(adata, resolution=1)
sc.tl.umap(adata)

# Plot 
sc.pl.umap(adata, color=['louvain', 'leiden', 'tissue', 'treatment', 'label_fine_haemosphere', 'sample_rep', 'cc_phase_class', 'pHb_RNA', 'pRb_RNA'], wspace=0.5, ncols=3)